System and method for a building-integrated predictive service communications platform

ABSTRACT

In general, certain embodiments of the present disclosure provide methods and systems for a building-integrated communication system. The system comprises a building, one or more processors, and memory. The memory includes one or more programs comprising instructions for transmitting one or more data packets over a network to one or more mobile user devices, the one or more data packets including identification information for a user; authenticating the user based on the identification information; monitoring actions of the user, wherein monitoring includes adding occurrence events and corresponding locations to a data base; and predicting a future action of the user based on the monitored actions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/441,083, entitled “System and Method for a Building-IntegratedPredictive Service Communications Platform,” filed on Dec. 30, 2016,which is incorporated by reference herein in its entirety for allpurposes.

TECHNICAL FIELD

The disclosed embodiments relate generally to digital communicationsystems.

BACKGROUND

With modern computing platforms and technologies being evermoreintegrated with the Internet, mobile device usage has becomeincreasingly popular. Current mobile device technologies strive toprovide convenience to everyday life. However, such conveniences havenot yet fully extended to living in multi-unit housing. For example,today, a person still has to physically go and check if a package hasbeen delivered to the building. Access into the building itself isusually limited to security access mechanisms that are not tied to anindividual's mobile phone. In addition, information about a building andother functions are not centralized and easily accessible to tenants.Common solutions offered are inadequate because they, for the most part,are not feed-based, are non-native, are not cloud-based, and do notallow staff/resident to submit and/or process requests in real time. Inaddition, current solutions provide systems in which requests have to gothrough “request managers” and have to be approved by staff members,which is inefficient and not cost-effective. Thus, there exists a needfor seamless and automated integration of building functions andinformation with mobile devices in order to increase convenience ofliving in multi-unit buildings.

SUMMARY

In general, certain embodiments of the present disclosure providemethods and systems for a building-integrated communication system. Thesystem comprises a building, one or more processors, and memory. Thememory includes one or more programs comprising instructions fortransmitting one or more data packets over a network to one or moremobile user devices, the one or more data packets includingidentification information for a user; authenticating the user based onthe identification information; and monitoring actions of the user,wherein monitoring includes adding occurrence events and correspondinglocations to a data base; and predicting a future action of the userbased on the monitored actions.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular embodiments of the present disclosure.

FIGS. 1A-1B illustrate an block diagram of an examplebuilding-integrated system, in accordance with various embodiments ofthe present disclosure.

FIG. 2 illustrates a block diagram of an example system infrastructure,in accordance with various embodiments of the present disclosure.

FIG. 3 illustrates an example login token, in accordance with variousembodiments of the present disclosure.

FIG. 4 illustrates an example method flow chart, in accordance withvarious embodiments of the present disclosure.

FIG. 5 illustrates one example of a neural network system that may makemultiple predictions, in accordance with one or more embodiments of thepresent disclosure.

FIG. 6 illustrates a particular example of a system that can be usedwith various embodiments of the present disclosure.

DESCRIPTION OF EMBODIMENTS

It will be understood that, although the terms “first,” “second,” etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first contact could be termed asecond contact, and, similarly, a second contact could be termed a firstcontact, without changing the meaning of the description, so long as alloccurrences of the “first contact” are renamed consistently and alloccurrences of the second contact are renamed consistently. The firstcontact and the second contact are both contacts, but they are not thesame contact.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the claims. Asused in the description of the embodiments and the appended claims, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willalso be understood that the term “and/or” as used herein refers to andencompasses any and all possible combinations of one or more of theassociated listed items. It will be further understood that the terms“comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “in response todetermining” or “in accordance with a determination” or “upon detecting”or “in response to detecting” that the stated condition precedent istrue, depending on the context.

Reference will now be made in detail to various embodiments, examples ofwhich are illustrated in the accompanying drawings. In the followingdetailed description, numerous specific details are set forth in orderto provide a thorough understanding of the present disclosure and thedescribed embodiments. However, the present disclosure may be practicedwithout these specific details. In other instances, well-known methods,procedures, components, and circuits have not been described in detailso as not to unnecessarily obscure aspects of the embodiments.

FIGS. 1A-1B illustrate an block diagram of an examplebuilding-integrated system 100, in accordance with various embodimentsof the present disclosure. System 100 includes building 102, iOS 110,Android 120, Web Application 130, and cloud-based management system 140.

In some embodiments, iOS 110 is implemented on a user device 106. Insome embodiments, iOS 110 provides the user with an interface configuredto communicate with other users, cloud 140, or even building 102. iOS110 includes various functions implemented through separate modules. Insome embodiments, the functions/modules include vehicle retrievalrequest 111, amenities reservation 112, visitor list creation/management113, amenity booking 114, work order submission 115, door opening 116.

In some embodiments, system 100 also includes Android 120. In someembodiments, Android 120 is implemented on a user device. In someembodiments, Android 120 provides the user with an interface configuredto communicate with other users, cloud 140, or even building 102.Android 120 includes various functions implemented through separatemodules. In some embodiments, the functions/modules include vehicleretrieval request 121, amenities reservation 122, visitor listcreation/management 123, amenity booking 124, work order submission 125,door opening 126. In various embodiments, the functions and modules inAndroid 120 are analogous to the functions and modules previouslydescribed for iOS 110.

In some embodiments, users may not have access to a mobile device andthus would require access to the private system via a Web Application130. In some embodiments, Web Application 130 provides the user with aweb-based interface configured to communicate with other users, cloud140, or even building 102. Web Application 130 includes variousfunctions implemented through separate modules. In some embodiments, thefunctions/modules include vehicle retrieval request 131, amenitiesreservation 132, visitor list creation/management 133, amenity booking134, work order submission 135, door opening 136. In variousembodiments, the functions and modules in Web Application 130 areanalogous to the functions and modules previously described for iOS 110.However, in other embodiments, since Web-based applications may not beas effective for authenticating a user for entrance into building 102,web application 130 may not have door opening 136 function.

In some embodiments, system 100 includes backend cloud-based managementsystem 140. In some embodiments, cloud 140 is implemented using one ormore servers 104. As with the other components of system 100, cloud 140includes many individual modules for carrying out certain functions.FIG. 1B illustrates some of the different functions/modules included inexample cloud 140. In some embodiments, cloud 140 includes unit datamanagement 141, resident data management 142, work order management 143,visitor management 144, amenity booking management 145, platformadvertisement management 146, event management 147, and valet management148.

System Infrastructure Components

FIG. 2 illustrates a block diagram of an example system infrastructure,in accordance with various embodiments of the present disclosure.

In various embodiments, the system infrastructure 200 includes a clientdevice 202 that connects to a backend cloud, such as cloud 140, over theinternet.

In some embodiments, client device 202 is a mobile phone, computer, orany mobile device running a mobile app, such as an iOS or Androiddevice, or a Web interface browser. In some embodiments, a user has toauthenticate himself into the system and then has access to thecentralized information and services, all on the same client device 202.In some embodiments, the backend cloud is a distributed system that'sincludes a load balancer 204, L1 (206) and L2 (208) content servers,application servers (210, 212, and 214), and storage servers (216 and218).

Authentication

In various embodiments, authentication is a key component into accessinga private network. FIG. 3 illustrates an example login token 300 usedfor authentication, in accordance with various embodiments of thepresent disclosure. In some embodiments, login token 300 is a datastructure storing key value fields for authentication. In someembodiments, token 300 includes user ID field 301, user permissionsfield 303, expiration timestamp 305, and hash 307. In some embodiments,timestamp 305 represents an expiration time for the token, which can berepresented in Unix milliseconds.

In various embodiments, once the user provides the login details to theapplication servers, these generate a login token with the useridentification, an expiration date for the token, user privileges forthe system and a hash of all the previous information. In someembodiments, the hash is computed using HMAC-SHA256.

In various embodiments, hash 307 is generated using a private key thatthe application server holds. In such embodiments, this has multipleadvantages. First, it ensures that only the application servers canchange the content of the token and if any third party attempts to doso, the application servers will be able to detect it. Second, the tokencontent is open and the client device can access the user id andpermissions, which provides a large advantage over otherimplementations. In some embodiments, the client device is able to storeuser credentials encrypted locally on the client device. The user canthen transparently access the locally stored token when needed. In suchembodiments, the client device is configured to automatically request(or request when necessary) a new token when the token expirationsdate/time has passed. In some embodiments, the client device supportsfingerprint authentication to unlock access to the encrypted credentialsor the token.

Communication

In some embodiments, the client device communicates with the serversending TCP/IP or UDP packets of data. More specifically HTTP could beused. In embodiments utilizing the cloud, the cloud offers a REST API(with GET, POST, DELETE, PATCH and UPDATE) that produces JavaScriptObject Notation content. In some embodiments, the client device is ableto receive these packets of data, join them together and parse theinformation from them. In some embodiments, the packets are transmittedover a wireless communication technology like WiFi or 4G.

Real Time

In some embodiments, cloud system 140 aggregates all the content andgenerates millions of unique feeds in real time. In certain embodiments,a push notification is sent to the client device and this retrieves theinformation from the cloud. In such embodiments, this “push-pull” modelminimizes bandwidth and optimizes battery life of the client devices. Insome embodiments, the system senses whether a client device is alreadyauthenticated/logged in to the private network. In such embodiments, newnotifications are not pushed to the client device if the device isdetected and determined to be logged in to the network. In someembodiments, the frequency/method of delivery for the “push-pull” modelcan be modified according to user preferences or location information(e.g. GPS location).

Aggregation

In some embodiments, cloud backend system 140 aggregates informationfrom different sources, e.g. databases, network storage, and theInternet. In some embodiments, for services and requests, system 140 isable to integrate and transact with third party systems over theInternet to provide a unified API of capabilities for a specificlocation. In such embodiments, this process is independent andtransparent of the actual provider of the service. In some embodiments,the cloud handles all transaction in real time and is able to retryunsuccessful operations until they succeed or fail.

Cloud Deployment

In some embodiments, cloud 140 is a distributed network of servers 104deployed in multiple geographical regions. In some embodiments, oneoptimization is to deploy cloud servers on both coasts of United States,central Europe, Australia, and South Asia. In various embodiments, cloudservers 104 are always close to main optic fiber deployments, tominimize the latency between the client device and the server.

Client Mobile Device Interface

In various embodiments, the client interface includes: centralizedaccess to all the data related to a place (e.g. building 102), itsproperties and services; one or more feeds of data personalized for auser for that current place; and one or more pages about the user andservices related to the user. In some embodiments, the centralizedinformation relating to a place is combined in a unified page withinformation like: name of the place, a photo or video of it, contactdetails (phone, email, SMS), amenities or special features related tothe place, services offered in that place, fitness events in that place,food and good ordering in that location.

In some embodiments, the feed is specially crafted for every user fromall the public events happening at that location/building and all theprivate events, requests, and actions occurring at thatlocation/building that are private to the user. In some embodiments, thefeed is ranked using a machine learning technique to optimize the orderhaving the most relevant content first. In some embodiments, the feed isunique to every user and every user has a unique feed. In someembodiments, different users have different permission levels and thefeed is crafted to respect and use this.

In various embodiments, the user interface includes a page about theuser and the actions the user can take in a specific place. In suchembodiments, this page contains the information about how the user islinked to the place and what kind of relationship they have. In variousembodiments, certain places will contain actions, e.g. pay rent, requestimprovements, or add another user that will have access to the place.

Client Web Interface

In some embodiments, the system also includes a web interface thatserves users, as well as places admins. In such embodiments, this webinterface provides access to all the functionality relevant to theplace/building. In some embodiments, it also has search and browsing forusers that are linked to the place/building and that have access to it.

Action Predictions

In various embodiments, the system or client device is able to learn theactions that the user performs over time. In some embodiments, this isbased on a rule based model and an advanced version of it on machinelearning techniques. For the rule based model, certain factors such asthe time of day, the day of the week, the weather, the location of theuser, the frequency with which the user accesses certain functions orapplications (e.g. over a threshold amount), and the frequency andcommonality of the user's actions right after certain trigger events(e.g. opening the door) are factored into a predictive algorithm. Insome embodiments, the predictive algorithm outputs a score. In someembodiments, the client device interface automatically puts into actionany “predicted” actions with a score above a certain threshold. In suchembodiments, there is a section in the client device interface thatpredicts the actions that the user would like to do perform in thecurrent location. In some embodiments, predictions are partially basedon occurrences or events at different locations, or the time of day andweather.

In some examples, the client device interface can remember applicationsfrequently accessed by a user when the user enters the building andprovide the applications automatically when the user enters thebuilding. In another example, the client device interface can recognizethat the user often scrolls down immediately after accessing “AmenitiesReservation” and provide several pages of scroll down AmenitiesReservation information to a user preemptively even before the userperforms any scrolling. However, if the user almost never scrolls down,then the client device interface recognizes this pattern of behavior andthen does not download scroll down pages in order to save resources,storage space, and time. Thus, the client device interface recognizesuser behavior pattern and then predicts the services that are needed,including predicting what not to do, based on past user behavior, inorder to make the systems and mobile devices run more efficiently (e.g.not waste resources and storage on actions that are not predicted to beneeded). In still other examples, the client device interface canrecognize user viewing or listening patterns during different times ofthe day and preemptively download media content information believed tobe of interest to a user. Similarly, the client device interface candetermine times when certain functions or modules of the application arefrequently accessed and provide preloaded and dynamically updated datafor those particular functions/modules based on frequency of viewing orthe time of day. For example, if the user always accesses “Visitor List”on the weekends, then the client device interface would recognize thispattern and pre-load up-to-date data on the visitor list (because thevisitor list is dynamic and always updating) onto the user's personaldevice. A wide variety of algorithms can be implemented to intelligentlyselect portions of application data for download to a mobile devicewithout having to download the entire library of data. Resources can beconserved while still providing the user with an active and dynamicexperience.

Machine Learning

According to various embodiments, the system may include an advancedneural network for predicting the actions of the user. In someembodiments, the neural network may use a labeled dataset to train theparameters of a neural network so that the neural network can accuratelyoutput correct predictions of actions that the user has not yetperformed. In some embodiments, examples of items in the dataset are themonitored actions and event occurrences identified and stored by thebuilding system. In some embodiments, the labeled dataset includesinformation regarding different locations, time of day and weather, andthe date.

According to various embodiments, the neural network may operate in twomodes. The first mode may be a “training” mode in which the initialparameters, which start from random values, are slowly updated by userconfirmation of predicted events. In some embodiments, the initialparameters updated, or trained, by stochastic gradient descent, whichslowly updates the initial random values of parameters such that theneural network outputs increasingly accurate predictions. The secondmode may be an “inference” mode in which the neural network applies theupdated parameters to predict actions based on new location and data. Insome embodiments, the predictions using new information that the neuralnetwork has never seen can also be an educated “inference” based on thesimilarity of variables in past datasets already processed by the neuralnetwork. In some embodiments, the training mode and the inference moderun simultaneously, with each mode helping the other. For example, insuch embodiments, new occurrence events are fed into the training modeto be used for future inference predictions. Similarly, inferencepredictions are assessed and confirmed by the user and used as feedbackto further sharpen the training mode.

According to various embodiments, the neural network may comprisemultiple layers, including a convolution-nonlinearity step and arecurrent step that are implemented in both the training mode and theinference mode. In some embodiments, the convolution-nonlinearity steptransforms input into tensors that the neural network may use to forwardpass through the layers. In some embodiments, the recurrent steptranslates the tensors from the convolution-nonlinearity step into thepredictions. Each step comprises a number of different computationallayers. A “layer” refers to a mathematical function which has one ormore input tensors and one or more output tensors. In some embodiments,a layer may include one or more tensors of parameters which are updated,or trained, using a stochastic descent algorithm. Such computationallayers may include convolutional layers, rectified-linear layers, andlinear layers. In other embodiments, other various layers may beimplemented in the neural network. In some embodiments, the network maybe built from a sequence of layers, with the output from one layerfeeding in as the input to the next layer.

Specialized Server

In some embodiments, the system 100 uses specialized servers to delivermultimedia images and videos to the user in the fastest way. In suchembodiments, the system includes L1 (206) and L2 (208) content servers.In some embodiments, L1 content servers 206 are strongly specialized inRAM DDR2000 memory. They have an optimized hashmap in memory that mapsfrom an URL to image objects. In some embodiments, L2 content servers208 are strongly optimized in disk space. In some embodiments, when animage is not found in the in-memory hashmap, a fallback request isperformed to L2 content servers 208 that contain all the media contentin a disk cache. In various embodiments, L1 servers 206 are deployedclose to the user geographically to minimize the latency and maximizethe perceived speed. In some embodiments, for unlocking doors we usecustom made Bluetooth or NFC locks that connect to phone 106 via a SSLencrypted connection. The first step is to retrieve the door token fromdistributed cloud storage 140. Then the token is presented to the doorlock via Bluetooth or NFC. The token is encrypted with AES256, AES 512or AES1024 using a private key that only the lock has. In otherembodiments, the locks on doors are controlled remotely through acellular network. In such embodiments, the user is first authenticated,then sends a request to unlock the doors via mobile device 106. Once therequest is received and processed by the system, the system determineswhether the user of the mobile device is authorized to open the door atthe particular time and location. If user is authorized, then the systemwill proceed with unlocking the door. Still in other embodiments, thelocks are controlled via the Internet or a combination of NFC,Bluetooth, cellular network, and Internet.

In some embodiments, the system includes a news board at the entrance ofthe building. In such embodiments, the news board is updated constantlyduring the day. In some embodiments, the news board provides informationonly, meaning it is not a portal for inputting requests to be handled orprocessed by the system.

Advantages

As described above, the systems and methods described in the presentdisclosure provide for an all-in-one building communications solutionthat centralizes data, management, and interaction within a singleplace. Such a system allows for staying up to date with everythingrelated to a particular place and requesting different servicessupported by the place. In various embodiments, just some of theadvantages of a system of the present disclosure include: unparalleledsecurity, streamlined resident-staff communication, increased residentretention, shortened maintenance cycles, potential full key elimination,efficient cloud-based solutions, native iOS and Android user-facing(resident) mobile apps, predictive service functionality, and multiple(over 30) APIs available for easy, “plug&play” integration.

FIG. 4 illustrates an example flow chart for a method managingbuilding-integrated communications within a building, in accordance withembodiments of the present disclosure. Method 400 only represents onepossible method for implementing the systems described herein. Otherpossible implementations that include only a subset of the operationscomprising method 400 may also be in accordance with embodiments of thespecification.

Method 400 begins with transmitting (401) one or more data packets overa network to one or more mobile user devices, the one or more datapackets including identification information for a user. Then, the useris authenticated (403) based on the identification information. Next,after authenticating the user, the actions of the user are monitored(405), wherein monitoring includes adding occurrence events andcorresponding locations to a data base. Last, a future action of theuser is predicted (407) based on the monitored actions.

In some embodiments, authenticating the user includes generating a useridentification token based on the identification information. In someembodiments, predicting the future action includes utilizing a rulebased modeling system. In some embodiments, method 400 further comprisesgenerating a personalized real-time feed for the user. In someembodiments, method 400 further comprises storing aggregated data frommultiple sources on cloud-based storage. In some embodiments, predictingthe future action includes utilizing machine learning techniques using aneural network. In some embodiments, a user interface is configured toallow for centralized access to all data related to the building,personalization of data feeds for the user based on the location of theuser within the building, and push notifications for the user based onthe user's location within the building.

In some embodiments, a neural network, may be able to make multipletypes of predictions. Examples of the various types of predictions mayinclude, but are not limited to: actions of the user during the day,actions of the user during the night, actions of the user during certainweather, etc. In various embodiments, substantial sharing of parametersoccurs between different layers. In some embodiments, some layers mayshare parameters between different types of predictions, while otherlayers may have distinct sets of parameters for each type of prediction.FIG. 5 is an illustration of one example of a neural network 500 thatmay make multiple types of predictions, in accordance with one or moreembodiments. Neural network 500 includes input parameters 510,convolution-nonlinearity step 520, recurrent step 532, recurrent step534, recurrent step 536, output prediction 542, output prediction 544,and output prediction 546. In some embodiments, input parameters 510 maybe input as third-order tensors into convolution-nonlinearity step 520.Parameter tensors within convolution-nonlinearity step 520 are sharedbetween prediction types. For example, there may be only one tensor usedfor convolution nonlinear step 520, regardless of how many differenttypes of objects are being detected by neural network 500.

The output of convolution-nonlinearity step 520 may be third-tensoroutput. This output may then be converted to a first-order tensor andinput into recurrent steps 532, 534, and 536. Each recurrent step 532,534, and 536 may output predictions 542, 544, and 546, respectively.Each output 542, 544, and 546 corresponds to one or more predictionsthat identify one possible user action. For example, output 542 maycomprise predictions for when it is raining. Output 544 may comprisepredictions for when the user enters the building in the morning. Output546 may comprise predictions for when the user receives a package but isnot in the building. Parameter tensors may be shared within eachrecurrent step, but may not be shared between recurrent steps.

In some embodiments, after the neural network has been sufficientlytrained (using a threshold number of datasets), the neural network canthen make new predictions in situations that the system has neverencountered during training. In such embodiments, the new situationsresemble or are in the same category as identified situations during thetraining phase. For example, if one identified situation in the trainingsets is what the user does when he walks in the door in the morning,then the neural network, in an inference mode, may predict what the userwill do when the user walks in the door at night, even if the neuralnetwork system has never encountered the situation where the user walksin the door at night before. This is because repeated passes of knowninput into the neural network trains the neural network to recognizedifferent versions of the situations as long as a threshold level ofsimilarity exists. In some embodiments, such a threshold is determinedby factor comparisons or variable differences. In some embodiments, suchthresholds for determining whether a situation leads to a prediction areautomatically set and/or inherently learned by the neural network systemthrough training and not by humans setting threshold values.

Various computing devices can implement the methods described. Forinstance, a mobile device, computer system, etc. can be used to generateartificially rendered images. With reference to FIG. 6, shown is aparticular example of a computer system that can be used to implementparticular examples of the present disclosure. For instance, thecomputer system 600 can be used to any of the methods according tovarious embodiments described above. According to particular exampleembodiments, a system 600 suitable for implementing particularembodiments of the present disclosure includes a processor 601, a memory603, an interface 611, and a bus 615 (e.g., a PCI bus). The interface611 may include separate input and output interfaces, or may be aunified interface supporting both operations. When acting under thecontrol of appropriate software or firmware, the processor 601 isresponsible for such tasks such as optimization. Various speciallyconfigured devices can also be used in place of a processor 601 or inaddition to processor 601. The complete implementation can also be donein custom hardware. The interface 611 is typically configured to sendand receive data packets or data segments over a network. Particularexamples of interfaces the device supports include Ethernet interfaces,frame relay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

In addition, various very high-speed interfaces may be provided such asfast Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces,HSSI interfaces, POS interfaces, FDDI interfaces and the like.Generally, these interfaces may include ports appropriate forcommunication with the appropriate media. In some cases, they may alsoinclude an independent processor and, in some instances, volatile RAM.The independent processors may control such communications intensivetasks as packet switching, media control and management.

According to particular example embodiments, the system 600 uses memory603 to store data and program instructions and maintained a local sidecache. The program instructions may control the operation of anoperating system and/or one or more applications, for example. Thememory or memories may also be configured to store received metadata andbatch requested metadata.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include hard disks,floppy disks, magnetic tape, optical media such as CD-ROM disks andDVDs; magneto-optical media such as optical disks, and hardware devicesthat are specially configured to store and perform program instructions,such as read-only memory devices (ROM) and programmable read-only memorydevices (PROMs). Examples of program instructions include both machinecode, such as produced by a compiler, and files containing higher levelcode that may be executed by the computer using an interpreter.

Although many of the components and processes are described above in thesingular for convenience, it will be appreciated by one of skill in theart that multiple components and repeated processes can also be used topractice the techniques of the present disclosure.

While the present disclosure has been particularly shown and describedwith reference to specific embodiments thereof, it will be understood bythose skilled in the art that changes in the form and details of thedisclosed embodiments may be made without departing from the spirit orscope of the disclosure. It is therefore intended that the disclosure beinterpreted to include all variations and equivalents that fall withinthe true spirit and scope of the present disclosure.

What is claimed is:
 1. A building-integrated communication system,comprising: a building; one or more processors; memory; and one or moreprograms stored in the memory, the one or more programs comprisinginstructions for: receiving one or more data packets over a network fromone or more mobile user devices, the one or more data packets includingidentification information for a user; authenticating the user based onthe identification information, wherein the user is authenticated usinga login token comprising a data structure storing key value fields forauthentication, the key value fields including: a user identification(ID) field, a user permissions field, an expiration timestamp, and ahash of all of the key value fields; and after the user has beenauthenticated, monitoring actions of the user, wherein monitoringincludes adding occurrence events and corresponding locations to a database; and predicting a future action of the user based on the monitoredactions, wherein the prediction predicts the actions that the user wouldperform at the specific location, wherein the prediction is performedusing a neural network.
 2. The system of claim 1, wherein authenticatingthe user includes generating a user identification token based on theidentification information.
 3. The system of claim 2, wherein predictingthe future action includes utilizing a rule based modeling system. 4.The system of claim 1, wherein the one or more instructions furthercomprises generating a personalized real-time feed for the user.
 5. Thesystem of claim 1, wherein the system uses cloud-based storage for dataaggregation from multiple sources.
 6. The system of claim 1, whereinpredicting the future action includes utilizing machine learningtechniques using a neural network.
 7. The system of claim 1, furthercomprising a user interface to allow for centralized access to all datarelated to the building, personalization of data feeds for the userbased on the location of the user within the building, and pushnotifications for the user based on the user's location within thebuilding.
 8. A method managing building-integrated communications withina building comprising: receiving one or more data packets over a networkfrom one or more mobile user devices, the one or more data packetsincluding identification information for a user; authenticating the userbased on the identification information, wherein the user isauthenticated using a login token comprising a data structure storingkey value fields for authentication, the key value fields including: auser identification (ID) field, a user permissions field, an expirationtimestamp, and a hash of all of the key value fields; and after the userhas been authenticated, monitoring actions of the user, whereinmonitoring includes adding occurrence events and corresponding locationsto a data base; and predicting a future action of the user based on themonitored actions, wherein the prediction predicts the actions that theuser would perform at the specific location, wherein the prediction isperformed using a neural network.
 9. The method of claim 8, whereinauthenticating the user includes generating a user identification tokenbased on the identification information.
 10. The method of claim 9,wherein predicting the future action includes utilizing a rule basedmodeling system.
 11. The method of claim 8, further comprisinggenerating a personalized real-time feed for the user.
 12. The method ofclaim 8, further comprising storing aggregated data from multiplesources on cloud-based storage.
 13. The method of claim 8, whereinpredicting the future action includes utilizing machine learningtechniques using a neural network.
 14. The method of claim 8, wherein auser interface is configured to allow for centralized access to all datarelated to the building, personalization of data feeds for the userbased on the location of the user within the building, and pushnotifications for the user based on the user's location within thebuilding.
 15. A non-transitory computer readable storage medium storingone or more programs configured for execution by a computer, the one ormore programs comprising one or more instructions for: receiving one ormore data packets over a network from one or more mobile user devices,the one or more data packets including identification information for auser; authenticating the user based on the identification information,wherein the user is authenticated using a login token comprising a datastructure storing key value fields for authentication, the key valuefields including: a user identification (ID) field, a user permissionsfield, an expiration timestamp, and a hash of all of the key valuefields; and after the user has been authenticated, monitoring actions ofthe user, wherein monitoring includes adding occurrence events andcorresponding locations to a data base; and predicting a future actionof the user based on the monitored actions, wherein the predictionpredicts the actions that the user would perform at the specificlocation, wherein the prediction is performed using a neural network.16. The non-transitory computer readable medium of claim 15, whereinauthenticating the user includes generating a user identification tokenbased on the identification information.
 17. The non-transitory computerreadable medium of claim 16, wherein predicting the future actionincludes utilizing a rule based modeling system.
 18. The non-transitorycomputer readable medium of claim 15, wherein the one or moreinstructions further comprises generating a personalized real-time feedfor the user.
 19. The non-transitory computer readable medium of claim15, wherein the one or more instructions further comprises furthercomprising storing aggregated data from multiple sources on cloud-basedstorage.
 20. The non-transitory computer readable medium of claim 15,wherein predicting the future action includes utilizing machine learningtechniques using a neural network.